Coronary artery disease (CAD) is a condition in which plaque builds up inside the coronary arteries. These arteries supply the heart muscle with oxygen-rich blood. Plaque narrows the arteries and reduces blood flow to the heart, which can cause angina or a heart attack. Over time, coronary artery disease may weaken the heart muscle and lead to heart failure and arrhythmias. Coronary heart disease is the most common type of heart disease. Lifestyle changes, medicines and/or medical procedures can effectively prevent or treat the disease in most people.
Historically, detection of coronary heart diseases involves patient history, physical examination, stress testing and possibly a coronary angiogram. During physical examination, a stethoscope is often used to examine the sound of the heart. Although the role of the stethoscope in the modern clinic seems to be fading, new electronic stethoscopes with integrated diagnostic algorithms might alter the trend and again expand the clinical potential of the stethoscope. An example of such a type of algorithms is a detector of coronary artery disease.
Four sounds may be generated during each heartbeat. The sounds are produced by blood turbulence and vibration of cardiac structures due primarily to the closing of the valves within the heart. These four sounds are identified as S1, S2, S3 and S4. S1 is usually the loudest heart sound and is the first heart sound during ventricular contraction. S1 is often described as a “lubb” sound. S1 occurs at the beginning of ventricular systole and relates to the closure of atrioventicular valves between the atria and the ventricles. S2 is often described as a “dubb” sound. S2 occurs at the beginning of the diastole and relates to the closing of the semilunar valves separating the aorta and pulmonary artery from the left and right ventricles, respectively. S1 and S2 can be easily heard with a stethoscope (“normal heart sounds”). S3 and S4, however, can usually not be heard in the normal heart (“abnormal heart sounds”) of a person over 40 years old. S3, also referred to as “ventricular gallop”, occurs in the early diastolic period and is caused by the ventricular wall distending to the point it reaches its elastic limit. S4, also referred to as “atrial gallop”, occurs near the end of atrial contraction and is also caused by the ventricular wall distending until it reaches its elastic limit.
Heart sounds can be used to augment the diagnosis and to help assess the severity of important types of cardiac disease. For example, after age 40, S3 can indicate congestive heart failure, and S4 can indicate hypertension, acute myocardial infarction, or coronary artery disease. Unfortunately, studies have shown that even highly experienced physicians do not reliably detect important heart sounds. Therefore various systems have been developed to support physicians in detecting possible heart diseases.
U.S. Pat. No. 7,096,060 relates to a method and system for automatically detecting heart sounds. The sound system may use ECG data to identify various locations, e.g. an R peak, within a beat and use those locations to assist in detection of heart sounds.
U.S. Pat. No. 5,159,932 relates to a myocardial ischemia detection system for non invasively monitoring the motion of the patient's heart, to detect and display ischemia induced variations in the heart's motion which indicate coronary artery disease.
In the system an accelerometer is used as a compression wave transducer which must have a wide bandwidth and should exhibit a flat frequency response from 0.025 Hz to 800 Hz. The purpose of the compression wave transducer is to translate with high accuracy the very low amplitude mechanical motion at the surface of the patient into an electrical signal for further processing. As a result, waveforms including identified morphological features are processed, displayed and used for diagnosis along with a reference ECG.
US-2009/0177107 relates to an electronic stethoscope system that automatically detects coronary artery disease in patients. The system uses an electronic stethoscope to record acoustic data from the fourth left intercostal space of a patient. A processing technique is then applied in order to filter the data and produce Fast Fourier Transform (FFT) data of magnitude versus frequency. If a bell curve is identified in the data between a predefined frequency range (e.g. 50 and 80 Hz) with a peak magnitude of greater than a predefined threshold, e.g. 2.5 units, the system automatically provides an output indicating that the patient is likely to have 50 to 99 percent stenosis of the coronary artery.
Studies have shown that diastolic sounds from CAD patients differ from non-CAD patients. This is described in U.S. Pat. No. 5,036,857. The difference is likely caused by weak murmurs originating from post-stenotic turbulence in the coronary arteries. One method for quantifying the difference in diastolic heart sounds is based on autoregressive (AR) models of the diastolic sounds based upon the fact that the pole magnitudes in AR-models of the diastolic heart sound in CAD patients differed from non-CAD patients.
U.S. Pat. No. 6,048,319 relates to a non-invasive acoustic screening device for detecting coronary stenosis by identifying S1 and S2 heart sounds, heart rate, and determines the diastolic interval of the subject and thereafter estimates the acoustic energy levels within a 2 octave band around approximately 20 Hz during diastole. Based upon such estimation, a diagnosis can be rendered as to the presence and degree of stenosis from the coronary artery.
Originally, the weak sounds were collected using very sensitive custom-made sensors, but the advent of electronic stethoscopes offers new opportunities. Advances of the electronic stethoscopes are portability, low cost and ease of use. The potential of implementing a CAD detection algorithm in electronic stethoscopes would yield an easily applicable CAD test. However the CAD related murmurs are very weak and the difference between CAD and non-CAD sounds is small. Detection algorithms are, therefore, likely to be sensitive to other types of noise, such as ambient noise and physiological noise, which will limit the usability of the method.
Based upon studies of the noise sensitivity of AR-models, when the models were applied for identification of CAD patients by analysis of heart sound recordings from an electronic stethoscope, the inventors have identified a need for improvement of the currently used analysis methods.
Thus, the object of the present invention is to provide an improved analysis system and method in order to improve diagnosis of CAD.